{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pandas.tools.plotting import scatter_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "%matplotlib inline\n",
    "pd.options.display.max_columns = None\n",
    "pd.set_option('display.max_colwidth', -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_datetime(date):\n",
    "    return datetime.date(int(date[:4]), int(date[5:7]), int(date[8:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "hp = pd.read_csv('/Users/Alex/Dropbox (Personal)/HPResults.csv')\n",
    "hp = hp[hp.iterations==2010]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = \"['mean_high_T1', 'TWN_high_T1', 'EC_high_T1', 'longitude', 'latitude', 'elevation', 'rolling_normal_high', 'current_temp_T1']\"\n",
    "B = \"['mean_high_T1', 'TWN_high_T1', 'EC_high_T1', 'longitude', 'latitude', 'elevation', 'rolling_normal_high', 'current_temp_T1', 'mean_high_T2', 'mean_high_T2_lkah']\"\n",
    "C = \"['mean_high_T1', 'TWN_high_T1', 'EC_high_T1', 'longitude', 'latitude', 'elevation', 'rolling_normal_high', 'current_temp_T1', 'mean_high_T2', 'mean_high_T2_lkah', 'mean_high_T1_2ago', 'TWN_high_2ago']\"\n",
    "def identify_fset(x):\n",
    "    if x == A:\n",
    "        return 0\n",
    "    elif x ==B:\n",
    "        return 1\n",
    "    elif x ==C:\n",
    "        return 2\n",
    "    else:\n",
    "        return 'UFO'\n",
    "    \n",
    "def identify_criterion(x):\n",
    "    if x == 'mse':\n",
    "        return 0\n",
    "    elif x == 'mae':\n",
    "        return 1\n",
    "    else:\n",
    "        return 'UFO'\n",
    "    \n",
    "def identify_date(x):\n",
    "    if x == \"2018-12-01\":\n",
    "        return 0\n",
    "    elif x ==\"2018-12-02\":\n",
    "        return 1\n",
    "    elif x ==\"2018-12-03\":\n",
    "        return 2\n",
    "    elif x ==\"2018-12-04\":\n",
    "        return 3\n",
    "    elif x ==\"2018-12-05\":\n",
    "        return 4\n",
    "    elif x ==\"2018-12-06\":\n",
    "        return 5\n",
    "    elif x ==\"2018-12-07\":\n",
    "        return 6\n",
    "    elif x ==\"2018-12-08\":\n",
    "        return 7\n",
    "    elif x ==\"2018-12-09\":\n",
    "        return 8\n",
    "    elif x ==\"2018-12-10\":\n",
    "        return 9\n",
    "    elif x ==\"2018-12-11\":\n",
    "        return 10\n",
    "    elif x ==\"2018-12-12\":\n",
    "        return 11\n",
    "    elif x ==\"2018-12-13\":\n",
    "        return 12\n",
    "    elif x ==\"2018-12-14\":\n",
    "        return 13\n",
    "    elif x ==\"2018-12-15\":\n",
    "        return 14\n",
    "    elif x ==\"2018-12-16\":\n",
    "        return 15\n",
    "    elif x ==\"2018-12-17\":\n",
    "        return 16\n",
    "    elif x ==\"2018-12-18\":\n",
    "        return 17\n",
    "    elif x ==\"2018-12-19\":\n",
    "        return 18\n",
    "    elif x ==\"2018-12-20\":\n",
    "        return 19\n",
    "    elif x ==\"2018-12-21\":\n",
    "        return 20\n",
    "    elif x ==\"2018-12-22\":\n",
    "        return 21\n",
    "    elif x ==\"2018-12-23\":\n",
    "        return 22\n",
    "    elif x ==\"2018-12-24\":\n",
    "        return 23\n",
    "    elif x ==\"2018-12-25\":\n",
    "        return 24\n",
    "    elif x ==\"2018-12-26\":\n",
    "        return 25\n",
    "    elif x ==\"2018-12-27\":\n",
    "        return 26\n",
    "    elif x ==\"2018-12-28\":\n",
    "        return 27\n",
    "    elif x ==\"2018-12-29\":\n",
    "        return 28\n",
    "    elif x ==\"2018-12-30\":\n",
    "        return 29\n",
    "    elif x ==\"2018-12-31\":\n",
    "        return 30\n",
    "    elif x ==\"2019-01-01\":\n",
    "        return 31\n",
    "    else:\n",
    "        return 'UFO'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "hp['fset']= hp['include_only_columns'].apply(identify_fset)\n",
    "hp['cr']= hp['criterion'].apply(identify_criterion)\n",
    "hp['sd']= hp['start_date'].apply(identify_date)\n",
    "hp['perf_TWN']=(hp.TWN_ave-hp.ML_ave)/hp.TWN_ave\n",
    "hp['perf_EC']=(hp.EC_ave-hp.ML_ave)/hp.EC_ave\n",
    "hp['perf_Mean']=(hp.perf_EC+hp.perf_TWN)/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "524\n"
     ]
    }
   ],
   "source": [
    "hpa=hp[['time_span','start_date','fset','sd','perf_TWN','cr']]\n",
    "hpa = hpa.sort_values('perf_TWN')\n",
    "print(len(hpa))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: 'pandas.tools.plotting.scatter_matrix' is deprecated, import 'pandas.plotting.scatter_matrix' instead.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1125b60b8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11256b320>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x110b289b0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x110fbf080>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x110fe2710>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x110fe2748>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x110f96470>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x111035b00>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1110651d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11108d860>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1110b5ef0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1110e45c0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11110dc50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11113e320>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1111659b0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x111196080>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1111c0710>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1111e7da0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x111217470>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11123eb00>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1115791d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11159d860>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1115c8ef0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1117215c0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x111747c50>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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J6hqK6L8+8raR2uObmvWtR7fJyen1ll49/o8XpLT793evVySe0Ord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\njGcH28PqCMXU3N2r5mDvkfE/bmvStoZ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3nj+ouzehn9Qe1C9uzdWF86qO1O54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Jw0j5MMr5MLTijm6vlj+Ma/NrG5po1QLMGkwnQWThjB/PF5LJyQj8NhWL1J76s7/rOZ379WhQms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\nIq1vbNdjr25RMuU649B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qcak4JvU2dKi8JK4NTRvV0ZtUZUmRNtd1afqYCv3h\n2XV6pb5VW9u6VVmSUG9vj9Y1d0uSNrZ0q6GtQcWJmF7emFJxPKb27pTiMdPmli4tWd+sqtIivbql\nVRWlCU0bXaH5L27Sc681qqmjR1vbujVn2miNqSzR5uYuSVJteZHGVZXuvQ0BiEBUXUBrteOzpynz\nuE//mgY95GJml5nZYjNbXF+fu7MGAOyaY6ePUjxzdHt0eXFOe3Vp+jhhVXHu8cLJNekvRYlYTLMm\nj8pqO2x81fbhg8dln6E68aAximU+3WsHWWZZUTzr//4qS4skpXcOpx48Jqut/3L6L1+Sjt9/rOKZ\nZY6tKMmZb3HmDEpJIneZfeslbqZjp2e/zimjdwSSGQPOip1xaN324aqS3PVXWZJeVnVZUU7bqPKi\n7cs8bmr2sdCDxu9YztFTstuOnbbj/ZxUm/2ltbi4WLWZZY2pzF3vpUU7dr8nHpj9Og+akF6mSZo9\npSarbW6/bahvm+hTXl6u0sw6LS/JXbdjq9J1JGIxHTa5Oqd9YibwVZUkVFOWW/NIVpKIqTiRXufb\n/84y23csln6PSorjKoqbasuKt/+tjK8qVWVpQmaSmVRVumPbGldVovKSuExSPCYVxWKqKI2rsiSh\n4kRc1eUlKnmdb37p6WKqLCtRUb9vYNUVxaosTqi4KKayorhqS4tUGo9v/5IWk1SSMFWXFWlSTYkq\nimIqSVh6fpapxdLPicWk4nh6247FpEQ8HSyL41I8U3ciLpmZKkriqikrUllRTNWlRYrFpEmjylRe\nlFBxPKby4oTGV2X/PRcnYorFTKVFMZUWJ5SIS/GYqTge1/jqUpml12d1Zl2Pry5RbVmxEjFTcVFM\n+40u275O4zFT5SB/u8BIZ+6+7xdq9klJ9e5+m5m9U9IUd/9epu1hdz81M/yQu5/2evOaM2eOL168\nWJJ0wVfv0XNt0tunSz/4OF3sdlffmQK6f+6ZwdbfnDlz1Ld9sn4RlTfaNh9dsVl/W16v/3jbETnT\nNrZ06s5n1uvcIydpQm3uUfBfPbZKh02s0VHTRuW0fX/+S5KkywecVZSk+Us26JFX63X1+bk/9V6z\npV23P7lG7zl2qqaOLc9p/+ztT+nkg+p0wdH75bS93jIfXrZJT6/eqk+dfVhO28rNbbr1iVW69KTp\nGldbkdP+X39eolNm1OmEg8bltH3t7uclSV8ccBZPkn67eLUef2WrvvHeo3PaNjZ26k/Pr9c7j5qk\n2kHOMLzeMm96aLmKE7GcM5mS9PdXG/TCukZdcnJu29bWbi1cXq8zDq5TZUVuoPqXW57Q2w4bp/OP\nnZ7TdvPCV1RVUqR3z52a0/bA0o16aWOzPnHGwTltfe/nPx0/VWNrct/Pmx5eruP3H6PZU0dvH9e3\nfSaTSb20uVWTa8sKLgBKUndvSh09SdX0OwjQ2tWrRMxUWhRXZ09S7d29isdiKoqbGtq6td+o9Dps\n706fySsfcHBm5eZGLVyxVUdNrtbm9l4dOLZS8ZhrY1OnKkqKVZyQ/vjses2cWKvXtjbp3hc26vSZ\n49XRKc2eOkqdvUkdUFelhLl+tnCVTtx/jA6fUqNkStra3qnxVSXqTErmrmUbmtWdTKmmrFh1VWWq\nqylWXXmJVtS3ylMpvbixRZNri/VaY5eqShOaUl2u5Q1t2q+mVI1d3erocdVVFss9pfZuqTie0pbW\nHh0yvlLL69t19H41KikuUkkipkQ8pkTMFJe0fHOrUqmkqspLFJPp5Y2NeujlzTpu+mgdMrFWKza3\n6KBxVdra1qVkytXc0au6yhLNnFSjv6/ephnjqzSqskhdvSmVJuLa1NSpTU3tmjSqXJMy67els0dF\n8ZhKBxyI6v/ZKb1xL4s3wneCsA1l+xm47ZjZk+4+Z1emjSoAHiPpY+7+MTO7UdLP3f2JTNtdki5X\n+jeAP3L3C15vXv0DIJBvBu4ogHzBtol8xvaJfEUAxHCKKgBG0gXU3Z+S1GlmCyQlJa0xs6syzddI\n+o2k2yVdHUV9AAAAAFCIIuvY7O5XDhh1bWb8c5LevO8rAgAAAIDCxo3gAQAAACAQBEAAAAAACAQB\nEAAAAAACEclVQIeTmdVLWt1v1FhJWyIqZ1dQ39CMtPqOkfTU67TnG+rbc/lcm/TG2+buTh816nl9\nI72e3d0+d1e+rZ+hKqTXk++vhf368KK+PTewtmnuXrezJ/c34gPgQGa2eFcvgRoF6huakV7fSK8/\navlcXz7XJg29vnx7fdTz+qjn9eVbPUNVSK9npL2WfK+X+oYmn+sbSm10AQUAAACAQBAAAQAAACAQ\nhRgA50VdwBugvqEZ6fWN9Pqjls/15XNt0tDry7fXRz2vj3peX77VM1SF9HpG2mvJ93qpb2jyub49\nrq3gfgMIAAAAABhcIZ4BBAAAAAAMggAIAAAAAIEgAAIAAABAIBJRF1DozOxYSSdIqpXUKOkxd18c\nbVU75Ht9+Y71h3xmZpXKbJvu3rqb07JtY0jYhgAMZij7JgyPEX8RGDOLS3qHBuxkJP3O3Xsjru07\nkkokzZfUJKla0lmSet39yihrk0ZEfXn73kpvvP7MbKakr0mqkWSSPPO8q939uWiq3sHM3ufuvzaz\nqZK+JWmCpG2SPufuS6OtTmL9Dam2MyR9SVJz5l+1pCpJX3f3+bswfd5+NmS2i5mSXnH3v0dUQ159\nNplZrbs3Zobfrsz6kXSHR7STz6dtyMwqJF0sKSnpVndvzoz/mLvftC9rGQ75/tm4OzKv5cuSeiV9\nz90fyYz/obt/IsraBpPv697MPuXu3zWz2ZK+r3R9CaX3SwuirW7o+6a9bQSsv+Hb/tx9RP+T9AtJ\nn5V0jKQDJR2deXxLHtT2t90ZT30j573dlfUnaYGkiQPaJklaEHXtmVoeyPz/e0lvzgwfIunhqGtj\n/Q25toWSygeMq5C0aBenz6vPBkn3Zv7/lKS7JX088/lwXUT15NVnU79t8TpJP5R0jqSvSro5inry\nbRuS9AdJH5H0T5IWSTq1/3obaf/y/bNxd7eTzN/QNEm3SroqM/7BqGsbieu+32fBXyQdlBkeu6uf\n/fugviHtm1h/w7f9FUIX0Onu/qEB4542s8iTuqTFZnaTpPu140jHmZKeirSqHfK9vnx+b6VdW382\nYBobZFxUyszsAElj3X2RJLn7S2aWT78NZv3tmS5Js5Q+K9XnSEmduzh9vn02FGf+v0jS6e6ekvQj\nM1sYUT35+tl0orufmhm+18weirCWfNqGqt39p5JkZr+V9FMzmxVBHcMpnz8bd0fM3V/JDH/AzK40\ns99IKo+yqDeQz+t+dOYs22h3XyFJ7r7FzPKlu99Q9017W76vP2mYtr9CCIB/MLM/SnpIO3Yypyp9\nxC9S7v5pMztO0hmSipTu4rDa3a+PtrK0TH1HS3qTpIOUPo283t2/Gm1l2+3svb07yqL6+W+lu/2d\noPT6i0laLembmfaPS/qBmdVqxwWXGiTlS7eWZUp3xXiprwuZmVUp3Y0xH7D+9twHJX3OzL6u9LpL\nSXpO0od3ZeJ+nw3HS5qh9GfDPEW3zzjczP5P6TMFJZI6Mv6Gt/sAAA0sSURBVONLI6on3/Y7x5jZ\n35ReT33bYkzprlWRyLP9X6+ZjXf3TZ7+vdF7zeyrkk6MoJbhkO+fjbvjBTOb5u6rJcndbzCzFyV9\nL+K6dqZv3Y9Set278mvd3yXpZEl3D9gvvRBxXX3675viSu+bntUu7pv2gXxff8O2/Y343wBKkpmd\nIulwpX+H0Szp75IOcPfHI67rp5nBbknjJK1Tur5x7n5ZZIVlZI5Wu7KPHBwuaYm7nxJNVdnMrE7S\nHEnHKv2blhUe0e9+BjKzB9z9DDP7nqR2SQ9IOkrSHHf/h2irA/bcTs5imtJdMd8SQT3T+j3c4O7d\nmYsIfNrdv7Kv68nUlFf7ncxvQ5Lu/mLmcbmkWe7+2OtPudfqyZv9X2Zb6XH3rgHjJ7r7hn1ZCwDk\ngxF/BtDMvqX0zqVX6X66/+zu9ZkuBGdEWly6//CpkmRmz7v7uzLDD0Zb1nZ3Spot6efu/pAkmdmf\n3f1tkVaVYWb3uvs5ZnaI0mcitki6wsxec/fPR1yelD5yJUmHu/tZmeG/vNH7a2afd/fr9m5pe476\nhiaf6zOzG3zXLsDRquwuOlI6AEbVbW5tViHpgNqh9JHafS7f9jv96zGz/vV8PYp6MvJm/+c7v8rg\nUZIKJgDm82fP7srX1zLIRThSSh/YyJeLwOT1RXX6XTxtP0nfljRe6YNokV88TZLM7ClJf5R0p7s/\nE3U9Aw3n9jfiA6CkuX1nqzJ9+m83s3+LuKY+/dfvF/oN50VfcXf/jpkVS/qImX1c6R9g55N8+93P\nQP9rZj+RtNbMbpH0sNJfkLdf5twGuQy6pJ8OMq9I5Ht9O5Ev7//O5EV9ZnaE0meElvUbvat/4y9K\nusjdmwbM8/7hqm839QXSvqueSdEG0nzb7+RbPVKe7/8yUm/8lPw0Qj+7BzXCXssPJb3X3df3jTCz\nSZJ+o4gOSA1wo6RLlA6A15nZ6e5+raRDoy1ru8sk/VrSDyR9w90XZQ7yz1O6G33UOpX+nfL/y4St\nhyTd5e55sV/XMG5/hRAA42ZW7O7d7v6cmV0k6RZJR0RdmKTLzCzu7kl3v1uSMoHr2xHXtZ27d0v6\noZn9WNKHlO6LnS/y7Xc/Wdz9F2b2V0lnK30UKyHpJ+7+rJRzGfQXlf6dUN8Hcz7cZiPf69tZN8Sr\nJe3zbog5heRxfZkzQuMl9fQ/I6T0VSJ35YzQ27Xj762/qHoH5Fsgzbf9Tr7VI+XZ/s/MEkp/Ce4L\nGcvc/b4oahmqfP/s3h0F8lry6SIw+X5RnXy+eJokdbr77yT9LvOZcZqk92d6zxwbbWk7tUfb34j/\nDWDmR+ar3H1zv3FxSe9x919HVxmGasDvfta7e0/mtxwnu/ufo6prV5nZ3wb7LaWZPdzvSn2RGQH1\ntWsnZ33cfUxkhfUVksf19X9vM2eEvifp35Q+4hp11/jdZmYTJTVkDlj1H5/waO67l1f7nXyrJ9+Y\n2YckfVTSM9px0Z7Zkn7q7r+IsrY9ke+f3btjpL2WTM+Kryp9IKH/BXi+7O7PR1ZYhpn9SOnb46zu\nN+6tSncHjfwsoJndnBl0pX/D3XeRlV+6+wURliZJMrNfufv7o65jZ4Zz+xvxARDIV2b2baXvbzPw\nMuhd7v6pKGuTRkR9T0o6Y7CzPlFciGSgfK7PzBYp3W26O/N4lNJnhOa4+/goawP2NUtf8OwU7/eF\nJxOQH3b3k6KrbM/k+2f37iik1wKMJARAYC+yHZfSr1X6UvqPuvvT0Va1Qz7Xl29nfQbK5/o4IwTs\nYGZ/kPQrZYeMsyT9o7ufH2VteyqfP7t3VyG8lny9aE0f6huaQqyPAAgAAApW5qcDl2pHyGiU9KjS\nXUBboqwNI89OLlqzpv8BtyhR39Dke32DMbOT3X3Bbk1DAAQAYPiY2WmS/s3d3x51LciV77/zQf4a\ncNGaJu04m9y7i7fY2auob2hGQH3Ddo/eQrgKaDDMrFbSB9z9xsxlX7/n7u+Oui4AAEYQfgeLPXXs\nIBetucvMHo6kmlzUNzT5Xt+w3RKJADiy1Er6F0k3Zu4BQvhDwTCzKyR9QtJT7v6PuzjN9oMie7U4\noB8zq5B0m6QpkuJKX5WtSdJ3JbUrT+4FCWDYLTazm5R70Zp8+c0i9Q1Nvtc3bLdEogvoCGJmv5Z0\noaSXJC2XdJi7zzSziyW9Q+krac2Q9E2lb6L+IUldks51961mdqCk/5FUp/SXlEsH3CS6/7LeI+ka\nSUlJTe5+SmY5F0mqkTRZ0i3u/p+Z5/9O0n5K36PvBneflxnfKukG7biv2IXuvmk41wsKg5ktk3SW\nu7+2G9NMl/RHd5+5t+oCBjKzd0k6x90vzTyukfSC0vdYXKH0TXnL6QKan8zsgZF4OxTkh3y/aA31\nDU0+1zecF58jAI4g/b/sDhi+WNIXJR2tdABbIek/3P1Hmf7Mq939u5a+afnH3X25mb1J6XvFDLoT\nNLPnlf6Cs87MajP3arlY6RtJz1Q6QP5d0sXuvtjMRmdCZllm/Knu3mBmLukCd7/bzL4hqdndv7Z3\n1hBGqsy9i/5Z6YMbdyh9oEFKd3E4xd1bzOyzkv5B6f75d7n7NQMOitzv7p/d99UjNGZ2sKS/KB30\n/iipReku+X33XrxA0mUEwPxkZg+6++lR1wEAUaELaOF4MHM1sxYza5J0d2b885JmZa6CdqKk282s\nb5qS15nfIkk/N7PbJN3Zb/z97t4gSWZ2p6STJC2WdIWZ9X1p30/pM5ENkrqV/oIkSU9Kivz+bcg/\n7v5xMztH0umSbv7/7d1vqN5jHMfx98efsZqEzZOVSGNjGDZre6BoCA8mWcm/JzxQY/4mmaRYSJYS\nmgeYNsxCiZCI1jFHYXOs5c8if1K2kM1szfbx4Lq2c3Y6y9nddn5n5/d51an7z3V3X3e7W7/vfX2v\nzwXMtd1Vv7db6kG2E4BzKP3ub0g6F7gbmGx7SlNzj/ax/Y2ks4BLgAeB9xueUuydK5ueQEREk1IA\njhxb+9ze0ef+Dsq/80HAn4O9UK4X5NOBS4HPaiwu9G463TW0Jt7NAmbY3izpQ8pKJMC2Pofvbiff\nufh/XcBCSUuB12z/XAvAC+ntwx9DKQh/bGiO0WI1hOt320sk/QncBBwv6UTb64AkTA5j2YYQEW2X\ni/EDy0bgiE5eaPsvSd9LmmN7ucoy4Om2Vw80vl7IdAPdki6mrOoBXCDpaMp+vssobXvjgT9q8TeR\n0jsd0RHbD0t6i7K60iXpIsqq30O2F/UdW1uhI4baacCjknYA2yjhRWOBtyRtBlbQ4f/VERER+1sK\nwANI3VPXJekrShLQ3roaeFrSvcChwMvAgAUg5eJmAuXC+/06bgrwKfAqJf1uSd3/1wPcKGktZS/W\nJx3MLQLY9eNDD9AjaRowEXgXeEDSUtubJI2nXHh3/KNIRKdsv0v5TvY3cajnEhExUnQSZhKdSQhM\nDFoNgZlq+6am5xIjj6QfgKmU9NnzKO3LayhBQ1sl3QLcUIdvAq6xvU7Si5QzcN5OCExERMTwJek6\n4E7KlqIvKduDtlCCDLts397g9FojBWAMWgrAiIiI4UfSOErg2ihgnu0V/Z7vpgS/HQ2MBn6pTz0H\nnGD71jpuEXCi7Vn1/s3ABNvzaqr3Qtt31OfuBMbYvn9/f74YGSSdCrwOzLS9oW4pWkhpoZ9te3uj\nE2yRg5qeQDRL0nxJq/r9zR9orO3nU/xFREQMH5IOoRxW3WP7zP7FH4Dt6TUE7j5gme0p9X4XJSF8\npzOAIyUdXO/PBD6ut7cCl0sau78+S4x45wPLbW8AsP17fXx5ir+hlT2ALWd7AbCg6XlERES0VQ20\neodyXNJZlPb364BJlBWSMcAGSkv8rzVtexXlKKaXgFuA0ZKmUhK5/xnkW68CTqpn+I6iBLx9Rwk6\nWkUpAO+qY/8FngFuAwb8oTiiQ383PYG2yQpgRERERPNOBp6yPQn4C5gLPAFcYfts4Fl2/8F2lO2p\nth9j95W9wRZ/1MCNL4BplATvbkqQ28watiXbP/V5yZPA1ZKO7PhTRpt9AMyRdAxAbQGNBmQFMCIi\nIqJ5P9nuqreXAPcAk4H3yslNHAz82mf8sn30vh9TVvpGAyuBb+t7r6e3/RPYdaTUC8A8ymphxKDZ\nXiNpAfCRpO30nu0bQywFYERERETz+qfybQTW2J6xh/H7qm2uC7gROJyywrceOIUBCsDqceBzSoBM\nxF6xvRhY3PQ82i4toBERERHNO07SzmLvKkor5ridj0k6tKYo7msrKe2f42z/5hIPvx6YTSkOd1OD\nO14Brt8Pc4mIIZACMCIiIqJ5XwNzJa0FjqLu/wMekbSa3lCWfcr2H5SCb02fh1cCxwKr9/CyxyjR\n/RFxAMo5gBERERENqimgb9qe3PBUIqIFsgIYERERERHRElkBjIiIiBghJHUDh/V7+FrbPU3MJyKG\nnxSAERERERERLZEW0IiIiIiIiJZIARgREREREdESKQAjIiIiIiJaIgVgRERERERES/wHmPOqEo55\nCPoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x2160 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_matrix(hpa, figsize=(15,30), alpha=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1150ebd30>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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B/kHSiWS7DXhW0mEgImLDnFdn1uC89IlVk1ICY3NmVZiZWdUrZS2pn2ZZiJnNjZ7+Qbp7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\nzCwVB4aZmaXiwDAzs1QcGGZmlsr/B2JmdkX8seArAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hpa[(hpa.cr==1)&(hpa.fset==0)&(hpa.time_span==60)].plot(x='sd',y='perf_TWN',kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118364208>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0iHg2XfQcML1KZfWvC+AiSW2Srq9GVz+taZykVuB5kgE5fwHsSr9nBJWNyZZb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\ns8wcJmZmlpnDxMzMMnOYmJlZZv8fYMv7lXpBqLQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hpa[(hpa.sd==10)&(hpa.fset==2)].plot(x='time_span', y='perf_TWN',kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'TWN_EC_split'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m--------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-103-928085e4193e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhpa\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhpa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_date\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;34m'2018-12-11'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m&\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhpa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfset\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m&\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhpa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTWN_EC_split\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m0.8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   4370\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4371\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4372\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4373\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4374\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'TWN_EC_split'"
     ]
    }
   ],
   "source": [
    "hpa[(hpa.start_date=='2018-12-11')&(hpa.fset==(0 or 1))&(hpa.TWN_EC_split==0.8)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for date in hp.start_date.unique():\n",
    "    best_time_span = hp[hp.start_date==str(get_datetime(date)-datetime.timedelta(1))].sort_values('perf_TWN', ascending=False).head(1)['time_span']\n",
    "    best_TWN_EC_split = hp[hp.start_date==str(get_datetime(date)-datetime.timedelta(1))].sort_values('perf_TWN', ascending=False).head(1)['TWN_EC_split']\n",
    "    best_fset = hp[hp.start_date==str(get_datetime(date)-datetime.timedelta(1))].sort_values('perf_TWN', ascending=False).head(1)['fset']\n",
    "    print(date)\n",
    "    print(hp[hp.start_date==date].sort_values('perf_TWN', ascending=False).head(6)[['time_span','TWN_EC_split','fset','perf_TWN']])\n",
    "    print('best from yesterday:')\n",
    "    print(hp[(hp.start_date==date)&(hp.time_span==best_time_span)&(hp.TWN_EC_split==best_TWN_EC_split)&(hp.fset==best_fset)][['time_span','TWN_EC_split','fset','perf_TWN']])\n",
    "    print('\\n')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
